Datasets:

MFreidank
/

glenda

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README.md ADDED Viewed

	@@ -0,0 +1,255 @@

+---
+pretty_name: GLENDA - The ITEC Gynecologic Laparoscopy Endometriosis Dataset
+language:
+- en
+annotations_creators:
+- expert-generated
+language_creators:
+- machine-generated
+license:
+- cc-by-nc-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-classification
+task_ids: []
+dataset_info:
+- config_name: binary_classification
+  features:
+  - name: image
+    dtype: image
+  - name: metadata
+    struct:
+    - name: id
+      dtype: int32
+    - name: width
+      dtype: int32
+    - name: height
+      dtype: int32
+    - name: file_name
+      dtype: string
+    - name: path
+      dtype: string
+    - name: fickr_url
+      dtype: string
+    - name: coco_url
+      dtype: string
+    - name: date_captured
+      dtype: string
+    - name: case_id
+      dtype: int32
+    - name: video_id
+      dtype: int32
+    - name: frame_id
+      dtype: int32
+    - name: from_seconds
+      dtype: int32
+    - name: to_seconds
+      dtype: int32
+  - name: labels
+    dtype:
+      class_label:
+        names:
+          '0': no_pathology
+          '1': endometriosis
+  splits:
+  - name: train
+    num_bytes: 4524957
+    num_examples: 13811
+  download_size: 895554144
+  dataset_size: 4524957
+- config_name: multiclass_classification
+  features:
+  - name: image
+    dtype: image
+  - name: metadata
+    struct:
+    - name: id
+      dtype: int32
+    - name: width
+      dtype: int32
+    - name: height
+      dtype: int32
+    - name: file_name
+      dtype: string
+    - name: path
+      dtype: string
+    - name: fickr_url
+      dtype: string
+    - name: coco_url
+      dtype: string
+    - name: date_captured
+      dtype: string
+    - name: case_id
+      dtype: int32
+    - name: video_id
+      dtype: int32
+    - name: frame_id
+      dtype: int32
+    - name: from_seconds
+      dtype: int32
+    - name: to_seconds
+      dtype: int32
+  - name: labels
+    dtype:
+      class_label:
+        names:
+          '0': No-Pathology
+          '1': 6.1.1.1_Endo-Peritoneum
+          '2': 6.1.1.2_Endo-Ovar
+          '3': 6.1.1.3_Endo-TIE
+          '4': 6.1.1.4_Endo-Uterus
+  splits:
+  - name: train
+    num_bytes: 4524957
+    num_examples: 13811
+  download_size: 895554144
+  dataset_size: 4524957
+---
+# Dataset Card for GLENDA - The ITEC Gynecologic Laparoscopy Endometriosis Dataset
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+## Dataset Description
+- **Homepage:** http://ftp.itec.aau.at/datasets/GLENDA/index.html
+- **Repository:**
+- **Paper:** [GLENDA: Gynecologic Laparoscopy Endometriosis Dataset](https://link.springer.com/chapter/10.1007/978-3-030-37734-2_36)
+- **Leaderboard:**
+- **Point of Contact:** freidankm@gmail.com
+### Dataset Summary
+GLENDA (Gynecologic Laparoscopy ENdometriosis DAtaset) comprises over 350 annotated endometriosis lesion images taken from 100+ gynecologic laparoscopy surgeries as well as over 13K unannotated non pathological images of 20+ surgeries. The dataset is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition.
+**Description**
+Endometriosis is a benign but potentially painful condition among women in child bearing age involving the growth of uterine-like tissue in locations outside of the uterus. Corresponding lesions can be found in various positions and severities, often in multiple instances per patient requiring a physician to determine its extent. This most frequently is accomplished by calculating its magnitude via utilizing the combination of two popular classification systems, the revised American Society for Reproductive Medicine (rASRM) and the European Enzian scores. Endometriosis can not reliably identified by laymen, therefore, the dataset has been created with the help of medical experts in the field of endometriosis treatment.
+**Purposes**
+* binary (endometriosis) classification
+* detection/localization
+**Overview**
+The dataset includes region-based annotations of 4 pathological endometriosis categories as well as non pathological counter example images. Annotations are created for single video frames that may be part of larger sequences comprising several consecutive frames (all showing the annotated condition). Frames can contain multiple annotations, potentially of different categories. Each single annotation is exported as a binary image (similar to below examples, albeit one image per annotation).
+# TODO: FIXME: A bit more useful info on dataset case distribution class distribution and link  to original + preview link
+TODO: FIXME: A bit more useful info on dataset case distribution class distribution and link  to original + preview link
+### Supported Tasks and Leaderboards
+- `image_classification`: The dataset can be used for binary (no pathology / endometriosis) or multiclass image classification (No-Pathology, 6.1.1.1\_Endo-Peritoneum,  6.1.1.2\_Endo-Ovar,  6.1.1.3\_Endo-DIE, 6.1.1.4\_Endo-Uterus. These classes respectively correspond to: no visible pathology in relation to endometriosis, peritoneal endometriosis, endometriosis on ovaries, deep infiltrating endometriosis (DIE) and uterine endometriosis.).
+## Dataset Structure
+### Data Instances
+#### binary\_classification
+TODO DESCRIBE
+#### multiclass\_classification
+TODO DESCRIBE
+## Dataset Creation
+### Curation Rationale
+From the [official website](http://ftp.itec.aau.at/datasets/GLENDA/index.html)
+> The dataset is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition
+### Source Data
+#### Initial Data Collection and Normalization
+From the [official website](http://ftp.itec.aau.at/datasets/GLENDA/index.html)
+> The dataset includes region-based annotations of 4 pathological endometriosis categories as well as non pathological counter example images. Annotations are created for single video frames that may be part of larger sequences comprising several consecutive frames (all showing the annotated condition). Frames can contain multiple annotations, potentially of different categories. Each single annotation is exported as a binary image (similar to below examples, albeit one image per annotation).
+### Annotations
+#### Annotation process
+From the [official website](http://ftp.itec.aau.at/datasets/GLENDA/index.html)
+> Corresponding lesions can be found in various positions and severities, often in multiple instances per patient requiring a physician to determine its extent. This most frequently is accomplished by calculating its magnitude via utilizing the combination of two popular classification systems, the revised American Society for Reproductive Medicine (rASRM) and the European Enzian scores. Endometriosis can not reliably identified by laymen, therefore, the dataset has been created with the help of medical experts in the field of endometriosis treatment.
+#### Who are the annotators?
+Medical experts in the field of endometriosis treatment.
+### Personal and Sensitive Information
+[More Information Needed]
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
+## Additional Information
+### Dataset Curators
+[More Information Needed]
+### Licensing Information
+The dataset is exclusively provided for scientific research purposes and as such cannot be used commercially or for any other purpose. If any other purpose is intended, you may directly contact the originator of the videos, Prof. Dr. Jörg Keckstein.
+GLENDA is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0, Creative Commons License) and is created as well as maintained by Distributed Multimedia Systems Group of the Institute of Information Technology (ITEC) at Alpen-Adria Universität in Klagenfurt, Austria.
+This license allows users of this dataset to copy, distribute and transmit the work under the following conditions:
+* Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+* Non-Commercial: You may not use the material for commercial purposes.
+For further legal details, please read the [complete license terms](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
+For additional information, please visit the [official website](http://ftp.itec.aau.at/datasets/GLENDA/index.html).
+### Citation Information
+```
+@inproceedings{10.1007/978-3-030-37734-2_36,
+	abstract = {Gynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient's abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) -- an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.},
+	address = {Cham},
+	author = {Leibetseder, Andreas and Kletz, Sabrina and Schoeffmann, Klaus and Keckstein, Simon and Keckstein, J{\"o}rg},
+	booktitle = {MultiMedia Modeling},
+	editor = {Ro, Yong Man and Cheng, Wen-Huang and Kim, Junmo and Chu, Wei-Ta and Cui, Peng and Choi, Jung-Woo and Hu, Min-Chun and De Neve, Wesley},
+	isbn = {978-3-030-37734-2},
+	pages = {439--450},
+	publisher = {Springer International Publishing},
+	title = {GLENDA: Gynecologic Laparoscopy Endometriosis Dataset},
+	year = {2020}
+}
+```

glenda.py ADDED Viewed

	@@ -0,0 +1,254 @@

+__doc__ = """\
+GLENDA (Gynecologic Laparoscopy ENdometriosis DAtaset) comprises over 350 annotated endometriosis lesion images taken from 100+ gynecologic laparoscopy surgeries as well as over 13K unannotated non pathological images of 20+ surgeries. The dataset is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition.
+Description
+Endometriosis is a benign but potentially painful condition among women in child bearing age involving the growth of uterine-like tissue in locations outside of the uterus. Corresponding lesions can be found in various positions and severities, often in multiple instances per patient requiring a physician to determine its extent. This most frequently is accomplished by calculating its magnitude via utilizing the combination of two popular classification systems, the revised American Society for Reproductive Medicine (rASRM) and the European Enzian scores. Endometriosis can not reliably identified by laymen, therefore, the dataset has been created with the help of medical experts in the field of endometriosis treatment.
+Purposes
+* (endometriosis) classification (binary or using 4 pathological endometriosis categories)
+* detection/localization
+Overview
+The dataset includes region-based annotations of 4 pathological endometriosis categories as well as non pathological counter example images. Annotations are created for single video frames that may be part of larger sequences comprising several consecutive frames (all showing the annotated condition). Frames can contain multiple annotations, potentially of different categories. Each single annotation is exported as a binary image (similar to below examples, albeit one image per annotation).
+Disclaimer
+The dataset is exclusively provided for scientific research purposes and as such cannot be used commercially or for any other purpose. If any other purpose is intended, you may directly contact the originator of the videos, Prof. Dr. Jörg Keckstein.
+In addition, reference must be made to the following publication when this dataset is used in any academic and research reports:
+A. Leibetseder, S. Kletz, K. Schoeffmann, S. Keckstein and J. Keckstein. 2020. GLENDA: Gynecologic Laparoscopy Endometriosis Dataset. In Proceedings of the 26th International Conference on Multimedia Modeling, MMM 2020. Springer, Cham.
+"""
+import json
+from pathlib import Path
+import re
+import datasets
+DESCRIPTION = str(__doc__)
+LICENSE = "cc-by-nc-4.0"
+CITATION = "Leibetseder, A., Kletz, S., Schoeffmann, K., Keckstein, S., Keckstein, J. (2020). GLENDA: Gynecologic Laparoscopy Endometriosis Dataset. In: , et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_36"
+HOMEPAGE = "http://ftp.itec.aau.at/datasets/GLENDA/index.html"
+VERSION = "1.5"
+URLS = {
+    "endometriosis_classes": "http://ftp.itec.aau.at/datasets/GLENDA/downloads//Glenda_v1.5_classes.zip",
+    "no_pathology": "http://ftp.itec.aau.at/datasets/GLENDA/downloads/GLENDA_v1.5_no_pathology.zip",
+}
+CLASS_NAMES = {
+    "binary_classification": ("no_pathology", "endometriosis"),
+    "multiclass_classification": (
+        "No-Pathology",
+        "6.1.1.1_Endo-Peritoneum",
+        "6.1.1.2_Endo-Ovar",
+        "6.1.1.3_Endo-TIE",
+        "6.1.1.4_Endo-Uterus",
+    ),
+}
+ENDOMETRIOSIS_IMAGE_METADATA_REGEX = re.compile(
+    r"c_(?P<case_id>[0-9]+)_v_\(video_(?P<video_id>[0-9]+).mp4\)_f_(?P<frame_id>[0-9]+).jpg"
+)
+NO_PATHOLOGY_IMAGE_METADATA_REGEX = re.compile(
+    r"v_(?P<video_id>[0-9]+)_s_(?P<from_seconds>[0-9]+)-(?P<to_seconds>[0-9]+)/f_(?P<frame_id>[0-9]+).jpg"
+)
+class GLENDA(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="binary_classification",
+            description="Contains images without visible pathology in relation to endometriosis (label = 'no_pathology') and different endometriosis classes (label = 'endometriosis').",
+            version=datasets.Version(f"{VERSION}.0"),
+        ),
+        datasets.BuilderConfig(
+            name="multiclass_classification",
+            description="Contains images without visible pathology in relation to endometriosis (label = 'No-Pathology') and different endometriosis classes (label is exactly one of: 6.1.1.1_Endo-Peritoneum, 6.1.1.2_Endo-Ovar, 6.1.1.3_Endo-TIE, 6.1.1.4_Endo-Uterus).",
+            version=datasets.Version(f"{VERSION}.0"),
+        ),
+        # TODO: FIXME: Coming soon
+        # datasets.BuilderConfig(
+        #     name="object_detection",
+        #     description="Contains images without visible pathology in relation to endometriosis and different endometriosis classes with bounding box annotations.",
+        #     version=datasets.Version(f"{VERSION}.0"),
+        # ),
+        # datasets.BuilderConfig(
+        #     name="instance_segmentation",
+        #     # TODO
+        #     description="Contains images without visible pathology in relation to endometriosis and different endometriosis classes with instance segmentation annotations.",
+        #     version=datasets.Version(f"{VERSION}.0"),
+        # ),
+    ]
+    def _info(self):
+        features = {
+            "image": datasets.Image(),
+            "metadata": {
+                "id": datasets.Value(dtype="int32"),
+                "width": datasets.Value(dtype="int32"),
+                "height": datasets.Value(dtype="int32"),
+                "file_name": datasets.Value(dtype="string"),
+                "path": datasets.Value(dtype="string"),
+                "fickr_url": datasets.Value(dtype="string"),
+                "coco_url": datasets.Value(dtype="string"),
+                "date_captured": datasets.Value(dtype="string"),
+                "case_id": datasets.Value("int32"),
+                "video_id": datasets.Value("int32"),
+                "frame_id": datasets.Value("int32"),
+                "from_seconds": datasets.Value("int32"),
+                "to_seconds": datasets.Value("int32"),
+            },
+        }
+        task_templates = None
+        supervised_keys = None
+        if self.config.name in ("binary_classification", "multiclass_classification"):
+            class_names = CLASS_NAMES[self.config.name]
+            features["labels"] = datasets.ClassLabel(
+                num_classes=len(class_names),
+                names=class_names,
+            )
+            supervised_keys = (("image", "labels"),)
+            task_templates = [
+                datasets.ImageClassification(
+                    image_column="image", label_column="labels"
+                )
+            ]
+        else:
+            raise NotImplementedError()
+        return datasets.DatasetInfo(
+            description=DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=HOMEPAGE,
+            license=LICENSE,
+            citation=CITATION,
+            task_templates=task_templates,
+            supervised_keys=supervised_keys,
+        )
+    def _split_generators(self, dl_manager):
+        endometriosis_data_path = Path(
+            dl_manager.download_and_extract(URLS["endometriosis_classes"])
+        ).joinpath(f"Glenda_v{VERSION}_classes")
+        no_pathology_data_path = Path(
+            dl_manager.download_and_extract(URLS["no_pathology"])
+        ).joinpath("no_pathology", "frames")
+        coco_annotation_filepath = Path(endometriosis_data_path).joinpath("coco.json")
+        with open(coco_annotation_filepath, "r") as coco_annotation_file:
+            coco_annotations = json.load(coco_annotation_file)
+        category_id2_name = {
+            category["id"]: category["name"]
+            for category in coco_annotations["categories"]
+        }
+        image_filepaths, image_metadata, image_labels = [], [], []
+        for annotation, metadata in zip(
+            coco_annotations["annotations"],
+            coco_annotations["images"],
+        ):
+            image_filepaths.append(
+                endometriosis_data_path.joinpath(
+                    metadata["coco_url"],
+                )
+            )
+            regex_match = re.search(
+                string=metadata["file_name"],
+                pattern=ENDOMETRIOSIS_IMAGE_METADATA_REGEX,
+            )
+            metadata.update(
+                {
+                    field_name: int(field_value)
+                    for field_name, field_value in regex_match.groupdict().items()
+                }
+            )
+            # NOTE: Only defined for `no_pathology` images
+            metadata["from_seconds"] = None
+            metadata["to_seconds"] = None
+            _ = metadata.pop("metadata")
+            # This field does not accurately reflect the dataset license,
+            # so let's not include it to avoid confusion.
+            _ = metadata.pop("license")
+            image_metadata.append(metadata)
+            if self.config.name == "binary_classification":
+                _, positive_label_name = CLASS_NAMES[self.config.name]
+                image_labels.append(positive_label_name)
+            elif self.config.name == "multiclass_classification":
+                image_labels.append(category_id2_name[annotation["category_id"]])
+            else:
+                raise NotImplementedError()
+        max_id = max(metadata["id"] for metadata in image_metadata)
+        for image_id, image_filepath in enumerate(
+            no_pathology_data_path.glob("*/*.jpg"), start=max_id + 1
+        ):
+            image_filepaths.append(image_filepath)
+            image_filename_with_parent_folder = image_filepath.relative_to(
+                no_pathology_data_path
+            )
+            metadata = {
+                "id": image_id,
+                "width": image_metadata[-1]["width"],
+                "height": image_metadata[-1]["height"],
+                "file_name": image_filepath.name,
+                "path": f"frames/{image_filename_with_parent_folder}",
+                "fickr_url": None,
+                "coco_url": f"frames/{image_filename_with_parent_folder}",
+                "date_captured": None,
+            }
+            match = re.search(
+                string=str(image_filename_with_parent_folder),
+                pattern=NO_PATHOLOGY_IMAGE_METADATA_REGEX,
+            )
+            metadata.update(
+                {
+                    field_name: int(field_value)
+                    for field_name, field_value in match.groupdict().items()
+                }
+            )
+            # NOTE: Only defined for `endometriosis` images
+            metadata["case_id"] = None
+            image_metadata.append(metadata)
+            if self.config.name in (
+                "binary_classification",
+                "multiclass_classification",
+            ):
+                negative_class_label_name, *_ = CLASS_NAMES[self.config.name]
+                image_labels.append(negative_class_label_name)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_filepaths": image_filepaths,
+                    "labels": image_labels,
+                    "metadata": image_metadata,
+                },
+            )
+        ]
+    def _generate_examples(self, image_filepaths, labels, metadata):
+        for example_id, (image_filepath, label, image_metadata) in enumerate(
+            zip(image_filepaths, labels, metadata)
+        ):
+            with open(image_filepath, "rb") as image_file:
+                yield example_id, {
+                    "image": {"path": str(image_filepath), "bytes": image_file.read()},
+                    "labels": label,
+                    "metadata": image_metadata,
+                }